Applied BioMath, LLC Announces Participation at the Cytokine-Based Cancer Immunotherapies Summit

On November 29, 2021 Applied BioMath (www.appliedbiomath.com), the industry-leader in providing model-informed drug discovery and development (MID3) support to help accelerate and de-risk therapeutic research and development (R&D), reported their participation at the Cytokine-Based Cancer Immunotherapies Summit occurring November 30th to December 2nd, 2021 in Boston, MA (Press release, Applied BioMath, NOV 29, 2021, View Source [SID1234596215]).

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Fei Hua, PhD, VP of Modeling and Simulation Services at Applied BioMath will present "Application of Quantitative Systems Pharmacology (QSP) Models in the Development of Immunocytokines" at the conference on Thursday, December 2nd at 3:00 p.m. In the presentation, Dr. Hua will discuss how QSP models can be used to help understand pharmacokinetic (PK) and pharmacodynamic (PD) complexities of immunocytokines and accelerate R&D.

"Understanding complex modalities such as immunocytokines is extremely resource intensive and difficult to do without the use of mathematical models," said John Burke, PhD, Co-founder, President and CEO of Applied BioMath. "Mechanistic modeling approaches help project teams more quickly understand these complexities to help accelerate therapeutic design, development, and better enable translation into the clinic and provide clinical support."

Applied BioMath’s solutions, which focus on quantitatively integrating knowledge about therapeutics with an understanding of its mechanism of action in the context of human disease mechanisms, are leveraged across the entire R&D spectrum from early research through all phases of clinical trials. Their approach involves working with clients to develop the appropriate mathematical strategy for each unique project, with common biosimulation software and services strategies including systems pharmacology, mechanistic modeling, PKPD, bioinformatics, and clinical pharmacology.

SQZ Biotechnologies to Present at the 4th Annual Evercore ISI HealthCONx Conference

On November 29, 2021 SQZ Biotechnologies (NYSE: SQZ), focused on unlocking the full potential of cell therapies for multiple therapeutic areas, reported that management will be presenting at the 4th Annual Evercore ISI HealthCONx Conference on December 1st (Press release, SQZ Biotech, NOV 29, 2021, View Source [SID1234596214]). Armon Sharei, Ph.D., Chief Executive Officer, will present a corporate overview and the company will be hosting one-on-one meetings.

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PRESENTATION DETAILS

Wednesday, December 1
4th Annual Evercore ISI HealthCONx Conference
Fireside Chat
9:15-9:35 am ET
Webcast

Specific conference webcast details and the company’s corporate overview presentation will be available on the Investors & Media section of the SQZ website. The webcast will be available for 90 days following the presentation.

MEI Pharma to Host Webcast on November 30, 2021 at 8:00 a.m. ET Reporting Data from Ongoing Global Phase 2 TIDAL Study of Zandelisib in Patients with Follicular Lymphoma

On November 29, 2021 MEI Pharma, Inc. (NASDAQ: MEIP), a late-stage pharmaceutical company focused on advancing new therapies for cancer, reported that the company will host a live investor and analyst webcast on Tuesday, November 30, 2021 reporting on data from the ongoing global Phase 2 TIDAL study evaluating zandelisib in patients with relapsed or refractory follicular lymphoma (Press release, MEI Pharma, NOV 29, 2021, View Source [SID1234596213]). The webcast will also review the zandelisib program and provide a corporate update.

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A live audio webcast of the event will be available on the Investors section of MEI’s website at View Source A replay of the webcast will be archived on MEI’s website for 30 days following the event.

Ranok Therapeutics Announces Agreement with Pfizer on Targeted Protein Degradation

On November 29, 2021 Ranok Therapeutics (Hangzhou) Co. Ltd., an emerging biopharmaceutical company focused on developing breakthrough therapies for cancer and other serious diseases, reported an agreement with Pfizer Inc. to apply and evaluate Ranok’s CHAMP platform technology on an undisclosed cancer target (Press release, Ranok Therapeutics, NOV 29, 2021, View Source [SID1234596212]). Terms of the agreement were not disclosed.

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Ranok has developed a proprietary and innovative targeted protein degradation (TPD) platform technology, CHAMP (Chaperone-mediated Protein Degradation), which leverages Ranok’s founders’ expertise in protein homeostasis to take advantage of the cellular chaperone network, potentially improving drug safety and efficacy due to selective targeting of disease tissues.

"We are very pleased to work with Pfizer to explore the application of CHAMP to an emerging cancer drug target," said Kevin P. Foley, Ph.D., Co-founder and Chief Scientific Officer of Ranok Therapeutics. "This represents our first pharmaceutical company research relationship and is an important step forward in establishing CHAMP as a preferred technology in the rapidly-growing field of targeted protein degradation. We look forward to uncovering new benefits of CHAMP through this research."

This evaluation agreement builds upon Ranok’s recent momentum, including its $40 million Series B financing in August 2021.

Cancer Patients May Get Faster and More Personalized Treatment as Result of Collaboration Between GE Healthcare, University of Cambridge and Addenbrooke’s Hospital

On November 29, 2021 GE Healthcare, the University of Cambridge and Cambridge University Hospitals reported that they have agreed to collaborate on developing an application aiming to improve cancer care, with Cambridge providing clinical expertise and data to support GE Healthcare’s development and evaluation of an AI-enhanced application that integrates cancer patient data from multiple sources into a single interface (Press release, GE Healthcare, NOV 29, 2021, View Source [SID1234596211]).

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Building on research supported by The Mark Foundation for Cancer Research and Cancer Research UK, the collaboration aims to address the problems of fragmented or siloed data and disconnected patient information, which is challenging for clinicians to manage effectively and can prevent cancer patients receiving optimal treatment.

"Thanks to ever-improving technologies, we now generate increasing amounts of complex data for each patient with cancer. These include multiple imaging scans, digital pathology, genomic data, advanced blood tests and treatment information. Bringing all this data together to make precise and informed decisions for patients can be hard. We often do this inefficiently and miss important connections between the data," said Professor Richard Gilbertson, Director of the Cancer Research UK Cambridge Centre, and Head of the Department of Oncology at the University of Cambridge.

This new application would be designed using advanced software engineering and machine learning methods to integrate a variety of patient data including clinical, imaging and genomic data – from diagnosis through every stage of treatment – into one single location. The aim is to offer all medical teams involved in a patient’s cancer care – medical oncologists, clinical oncologists, surgeons, radiologists, pathologists, clinical nurse specialists and more – simultaneous access to the necessary data and information to allow the medical team to plan the best, most personalized treatment for each of their patients.

The application is expected to be evaluated for ovarian cancer initially in Cambridge and the goal is to evaluate it across the UK, and beyond. Ovarian cancer is often difficult to treat as most patients present with advanced disease. Although initially 70-80% of patients will respond well to chemotherapy, ultimately most develop chemotherapy resistance leading to treatment failure.1 The application may help clinicians have better visibility on how the patient respond to treatment, thus helping them more effectively identify when treatment may require adjustment. If the application is successfully developed, our vision is for it to be expanded for use in breast and kidney cancer patients.

"Healthcare professionals can struggle to easily find and interpret the many different types of patient data information they need to make the best clinical decisions," said Dr Ben Newton, GM Oncology at GE Healthcare. "Bringing these multiple data streams into a single interface could enable clinicians to make fast, informed and highly personalised treatment decisions throughout a patient’s cancer care pathway."

Two Addenbrooke’s cancer clinicians aiming to evaluate the application to help patients are consultant oncologist Prof. James Brenton, professor of Ovarian Cancer Medicine and a senior group leader at the Cancer Research UK Cambridge Institute; and consultant radiologist Prof. Evis Sala, professor of Oncological Imaging, University of Cambridge.

"Aggregating and analysing the substantial amounts of data available would help address an unmet need. Ovarian cancer is an important and complex disease with poor outcomes, and we believe this application would help us deal with its complexity. Eventually, we hope to be able to better understand the disease and therefore improve treatment and outcomes for patients," says Prof. Brenton, who co-leads the Mark Foundation Institute for Integrated Cancer Medicine (MFICM) at the University of Cambridge.

"If we can aggregate and integrate relevant data along the care pathway, and visualize the output, it may ultimately lead to clinicians making better-informed decisions and better care." adds Prof. Sala who also co-leads the MFICM at the University of Cambridge.

"The team aims to transform the delivery of cancer patient care by integrating multiple data streams together into a single platform that can be accessed simultaneously by clinicians, patients and multi-disciplinary teams (MDTs) from tertiary and regional hospitals."

The development work will be underpinned by GE Healthcare’s Edison platform to integrate data from diverse sources, such as electronic health records (EHR) and radiology information systems (RIS), imaging and other medical device data.